'''simple docstring'''




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__UpperCAmelCase					=logging.get_logger(__name__)  # pylint: disable=invalid-name






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    warnings.warn(
        '''The preprocess method is deprecated and will be removed in a future version. Please'''
        ''' use VaeImageProcessor.preprocess instead'''							,    UpperCamelCase__							,    )
    if isinstance(UpperCamelCase__							,    torch.Tensor							):
        return image
    elif isinstance(UpperCamelCase__							,    PIL.Image.Image							):
        __lowerCamelCase            =    [image]

    if isinstance(image[0]							,    PIL.Image.Image							):
        __lowerCamelCase      ,    __lowerCamelCase            =    image[0].size
        __lowerCamelCase      ,    __lowerCamelCase            =    (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        __lowerCamelCase            =    [np.array(i.resize((w, h)							,    resample=PIL_INTERPOLATION['''lanczos''']							)							)[None, :] for i in image]
        __lowerCamelCase            =    np.concatenate(UpperCamelCase__							,    axis=0							)
        __lowerCamelCase            =    np.array(UpperCamelCase__							).astype(np.floataa							) / 2_5_5.0
        __lowerCamelCase            =    image.transpose(0							,    3							,    1							,    2							)
        __lowerCamelCase            =    2.0 * image - 1.0
        __lowerCamelCase            =    torch.from_numpy(UpperCamelCase__							)
    elif isinstance(image[0]							,    torch.Tensor							):
        __lowerCamelCase            =    torch.cat(UpperCamelCase__							,    dim=0							)
    return image






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Dict:
    if isinstance(UpperCamelCase__							,    torch.Tensor							):
        return mask
    elif isinstance(UpperCamelCase__							,    PIL.Image.Image							):
        __lowerCamelCase            =    [mask]

    if isinstance(mask[0]							,    PIL.Image.Image							):
        __lowerCamelCase      ,    __lowerCamelCase            =    mask[0].size
        __lowerCamelCase      ,    __lowerCamelCase            =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        __lowerCamelCase            =    [np.array(m.convert('''L'''							).resize((w, h)							,    resample=PIL_INTERPOLATION['''nearest''']							)							)[None, :] for m in mask]
        __lowerCamelCase            =    np.concatenate(UpperCamelCase__							,    axis=0							)
        __lowerCamelCase            =    mask.astype(np.floataa							) / 2_5_5.0
        __lowerCamelCase            =    0
        __lowerCamelCase            =    1
        __lowerCamelCase            =    torch.from_numpy(UpperCamelCase__							)
    elif isinstance(mask[0]							,    torch.Tensor							):
        __lowerCamelCase            =    torch.cat(UpperCamelCase__							,    dim=0							)
    return mask




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	UNetaDModel
     lowerCamelCase    :	RePaintScheduler






     def __init__(					self						:						Dict    ,							a						:						Optional[int]    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         super().__init__()
         self.register_modules(unet=a    ,							scheduler=a       )






     @torch.no_grad()
     def __call__(					self						:						Tuple    ,							a						:						Union[torch.Tensor, PIL.Image.Image]    ,							a						:						Union[torch.Tensor, PIL.Image.Image]    ,							a						:						int = 2_50    ,							a						:						float = 0.0    ,							a						:						int = 10    ,							a						:						int = 10    ,							a						:						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							a						:						Optional[str] = "pil"    ,							a						:						bool = True    ,							):





         """simple docstring"""



         __lowerCamelCase            =    image

         __lowerCamelCase            =    _preprocess_image(a       )
         __lowerCamelCase            =    original_image.to(device=self.device    ,							dtype=self.unet.dtype       )
         __lowerCamelCase            =    _preprocess_mask(a       )
         __lowerCamelCase            =    mask_image.to(device=self.device    ,							dtype=self.unet.dtype       )

         __lowerCamelCase            =    original_image.shape[0]

         # sample gaussian noise to begin the loop
         if isinstance(a    ,							a       ) and len(a       ) != batch_size:
             raise ValueError(
                 f"""You have passed a list of generators of length {len(a       )}, but requested an effective batch"""
                 f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

         __lowerCamelCase            =    original_image.shape
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=self.device    ,							dtype=self.unet.dtype       )

         # set step values
         self.scheduler.set_timesteps(a    ,							a    ,							a    ,							self.device       )
         __lowerCamelCase            =    eta

         __lowerCamelCase            =    self.scheduler.timesteps[0] + 1
         __lowerCamelCase            =    generator[0] if isinstance(a    ,							a       ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
             if t < t_last:
                 # predict the noise residual
                 __lowerCamelCase            =    self.unet(a    ,							a       ).sample
                 # compute previous image: x_t -> x_t-1
                 __lowerCamelCase            =    self.scheduler.step(a    ,							a    ,							a    ,							a    ,							a    ,							a       ).prev_sample

             else:
                 # compute the reverse: x_t-1 -> x_t
                 __lowerCamelCase            =    self.scheduler.undo_step(a    ,							a    ,							a       )
             __lowerCamelCase            =    t

         __lowerCamelCase            =    (image / 2 + 0.5).clamp(0    ,							1       )
         __lowerCamelCase            =    image.cpu().permute(0    ,							2    ,							3    ,							1       ).numpy()
         if output_type == "pil":
             __lowerCamelCase            =    self.numpy_to_pil(a       )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=a       )

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations(UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item							) for item in array							)

    return count_of_possible_combinations(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase__							,    UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        __lowerCamelCase            =    sum(
            count_of_possible_combinations_with_dp_array(target - item							,    UpperCamelCase__							)
            for item in array							)
        __lowerCamelCase            =    answer
        return answer

    __lowerCamelCase            =    [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    [0] * (target + 1)
    __lowerCamelCase            =    1

    for i in range(1							,    target + 1							):
        for j in range(UpperCamelCase__							):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase					=3
  __UpperCAmelCase					=5
  __UpperCAmelCase					=[1, 2, 5]
  print(combination_sum_iv(n, array, target))
